A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting

• URL: http://arxiv.org/abs/2503.19943v1
• Date: Tue, 25 Mar 2025 10:14:54 GMT
• Title: A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting
• Authors: Sakshi Dhankhar, Stefan Wittek, Hamidreza Eivazi, Andreas Rausch,
• Abstract summary: In July 2017, the cities of Goslar and G"ottingen experienced severe flood events characterized by short warning time of only 20 minutes.<n>This highlights the critical need for a more reliable and timely flood forecasting system.
• Score: 0.9487148673655145
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Study Region: Goslar and G\"ottingen, Lower Saxony, Germany. Study Focus: In July 2017, the cities of Goslar and G\"ottingen experienced severe flood events characterized by short warning time of only 20 minutes, resulting in extensive regional flooding and significant damage. This highlights the critical need for a more reliable and timely flood forecasting system. This paper presents a comprehensive study on the impact of radar-based precipitation data on forecasting river water levels in Goslar. Additionally, the study examines how precipitation influences water level forecasts in G\"ottingen. The analysis integrates radar-derived spatiotemporal precipitation patterns with hydrological sensor data obtained from ground stations to evaluate the effectiveness of this approach in improving flood prediction capabilities. New Hydrological Insights for the Region: A key innovation in this paper is the use of residual-based modeling to address the non-linearity between precipitation images and water levels, leading to a Spatiotemporal Radar-based Precipitation Model with residuals (STRPMr). Unlike traditional hydrological models, our approach does not rely on upstream data, making it independent of additional hydrological inputs. This independence enhances its adaptability and allows for broader applicability in other regions with RADOLAN precipitation. The deep learning architecture integrates (2+1)D convolutional neural networks for spatial and temporal feature extraction with LSTM for timeseries forecasting. The results demonstrate the potential of the STRPMr for capturing extreme events and more accurate flood forecasting.

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